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2012 IEEE Fifth International Conference on Cloud Computing
Keeping Data Private while Computing in the Cloud
Honolulu, HI, USA USA
June 24-June 29
ISBN: 978-1-4673-2892-0
The cloud offers unprecedented access to computation.  However, ensuring the privacy of that computation remains a significant challenge.  In this paper, we address the problem of distributing computation onto the cloud in a way that preserves the privacy of the computation's data even from the cloud nodes themselves.  The approach, called sTile, separates the computation into small subcomputations and distributes them in a way that makes it prohibitively hard to reconstruct the data.  We evaluate sTile theoretically and empirically: First, we formally prove that sTile systems preserve privacy.  Second, we deploy a prototype implementation on three different networks, including the globally-distributed PlanetLab testbed, to show that sTile is robust to network delay and efficient enough to significantly outperform existing privacy-preserving approaches.
Index Terms:
Tiles,Assembly,Privacy,Crystals,Software systems,Data privacy,Computer architecture,sTile,privacy,privacy-preserving computation,private cloud computing,cloud,self-assembly,tile assembly model
Citation:
Yuriy Brun, Nenad Medvidovic, "Keeping Data Private while Computing in the Cloud," cloud, pp.285-294, 2012 IEEE Fifth International Conference on Cloud Computing, 2012
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